Quantum Topology: A Dynamic Learning Algorithm

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum topology, a fascinating field of theoretical physics, has recently garnered significant attention for its potential to revolutionize computational science and data analysis. This paper introduces a novel dynamic learning algorithm based on quantum topology, aiming to leverage the unique properties of quantum systems to learn intricate patterns and relationships within data. The core mechanism of this algorithm centers around the quantum entanglement of data points, enabling parallel computation and the identification of subtle correlations that traditional machine learning methods often miss. The proposed approach promises to enhance model learning capabilities and adaptability, offering a departure from static parameter adjustments. This work presents a framework for dynamically adjusting model parameters based on quantum topological principles, potentially leading to more effective data representation and insight extraction.

DOI: 10.5281/zenodo.22193984

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